Ma Jun, Li Yuhui, Zuo Yuanyang
College of Design and Innovation, Tongji University, Shanghai 200092, China.
School of Automotive Studies, Tongji University, Shanghai 200092, China.
Sensors (Basel). 2024 Dec 15;24(24):8010. doi: 10.3390/s24248010.
As the global traffic environment becomes increasingly complex, driving safety issues have become more prominent, making manual-response driving warning systems (DWSs) essential. Augmented reality head-up display (AR-HUD) technology can project information directly, enhancing driver attention; however, improper design may increase cognitive load and affect safety. Thus, the design of AR-HUD driving warning interfaces must focus on improving attention and reducing cognitive load. Currently, systematic research on AR-HUD DWS interfaces is relatively scarce. This paper proposes an ecological interface cognitive balance design strategy for AR-HUD DWS based on cognitive load theory and environmental interface design theory. The research includes developing design models, an integrative framework, and experimental validation suitable for warning scenarios. Research results indicate that the proposed design effectively reduces cognitive load and significantly decreases driver response and comprehension times, outperforming existing interfaces. This design strategy and framework possess promotional value, providing theoretical references and methodological guidance for AR-HUD warning interface research.
随着全球交通环境日益复杂，驾驶安全问题愈发突出，使得手动响应式驾驶预警系统（DWS）变得至关重要。增强现实抬头显示（AR - HUD）技术能够直接投射信息，增强驾驶员的注意力；然而，设计不当可能会增加认知负荷并影响安全性。因此，AR - HUD驾驶预警界面的设计必须注重提高注意力并降低认知负荷。目前，针对AR - HUD DWS界面的系统性研究相对较少。本文基于认知负荷理论和环境界面设计理论，提出了一种用于AR - HUD DWS的生态界面认知平衡设计策略。该研究包括开发适用于预警场景的设计模型、综合框架以及实验验证。研究结果表明，所提出的设计有效地降低了认知负荷，并显著缩短了驾驶员的反应和理解时间，优于现有界面。这种设计策略和框架具有推广价值，为AR - HUD预警界面研究提供了理论参考和方法指导。
